{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple drift detection example\n",
    "\n",
    "For a simple example, we'll use the [MMD detector](../cd/methods/mmddrift.ipynb) to check for drift on the two-dimensional binary classification problem shown previously. The MMD detector is a kernel-based method for multivariate two sample testing. Since the number of dimensions is already low, dimension reduction step is not necessary here here. For a more advanced example using the [MMD detector](../cd/methods/mmddrift.ipynb) with dimension reduction, check out the [Maximum Mean Discrepancy drift detector on CIFAR-10](cd_mmd_cifar10.ipynb) example. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LinearSegmentedColormap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The true model/process is defined as:\n",
    "\n",
    "$$\n",
    "y =\n",
    "\\begin{cases}\n",
    "    1 & \\text{if } x_2 > sx_1 \\\\\n",
    "    0 & \\text{otherwise}\n",
    "\\end{cases}\n",
    "$$\n",
    "\n",
    "where the slope $s$ is set as $s=-1$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.stats import multivariate_normal\n",
    "\n",
    "def true_model(X,slope=-1):\n",
    "    z = slope*X[:,0]\n",
    "    idx = np.argwhere(X[:,1]>z)\n",
    "    y = np.zeros(X.shape[0])\n",
    "    y[idx] = 1\n",
    "    return y\n",
    "\n",
    "true_slope = -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reference distribution is defined as a mixture of two Normal distributions:\n",
    "\n",
    "$$\n",
    "P_{ref}(\\mathbf{X}) = \\phi_1 \\mathcal{N}\\left(\\left[-1,-1\\right]^T, \\sigma^2\\mathbf{I} \\right) + \\phi_2 \\mathcal{N}\\left(\\left[1,1\\right]^T, \\sigma^2\\mathbf{I} \\right)\n",
    "$$\n",
    "\n",
    "with the standard deviation set at $\\sigma=0.8$, and the weights\n",
    "set to $\\phi_1=\\phi_2=0.5$. Reference data $\\mathbf{X}^{ref}$ and training data $\\mathbf{X}^{train}$ (see Note 1) can be generated by sampling from this distribution. The corresponding labels $y^{ref}$ and $y^{train}$ are obtained by evalulating `true_model()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference distribution\n",
    "sigma = 0.8\n",
    "phi1 = 0.5\n",
    "phi2 = 0.5\n",
    "ref_norm_0 = multivariate_normal([-1,-1], np.eye(2)*sigma**2)\n",
    "ref_norm_1 = multivariate_normal([ 1, 1], np.eye(2)*sigma**2)\n",
    "\n",
    "# Reference data (to initialise the detectors)\n",
    "N_ref = 240\n",
    "X_0 = ref_norm_0.rvs(size=int(N_ref*phi1),random_state=1)\n",
    "X_1 = ref_norm_1.rvs(size=int(N_ref*phi2),random_state=1)\n",
    "X_ref = np.vstack([X_0, X_1])\n",
    "y_ref = true_model(X_ref,true_slope)\n",
    "\n",
    "# Training data (to train the classifer)\n",
    "N_train = 240\n",
    "X_0 = ref_norm_0.rvs(size=int(N_train*phi1),random_state=0)\n",
    "X_1 = ref_norm_1.rvs(size=int(N_train*phi2),random_state=0)\n",
    "X_train = np.vstack([X_0, X_1])\n",
    "y_train = true_model(X_train,true_slope)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save time later on, a plotting function is defined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 16})\n",
    "\n",
    "def plot(X,y, slope, clf=None):\n",
    "    # Init plot\n",
    "    fig, ax = plt.subplots(figsize=(4,4))\n",
    "    #ax.axis('equal')\n",
    "    ax.set_xlabel(r'$x_1$')\n",
    "    ax.set_ylabel(r'$x_2$')\n",
    "    ax.set_xlim([-3.5,3.5])\n",
    "    ax.set_ylim([-6,3.3])\n",
    "    \n",
    "    # Plot data\n",
    "    cmap = LinearSegmentedColormap.from_list('my_cmap', [(0.78,0.44,0.22), (0.22, 0.44, 0.78)], N=2)\n",
    "    scat = ax.scatter(X[:,0], X[:,1],c=y,ec='k',s=70,cmap=cmap,alpha=0.7)\n",
    "    \n",
    "    # Plot true decision boundary\n",
    "    xconcept = np.array(ax.get_xlim())\n",
    "    ax.plot(xconcept,xconcept*slope,'k--',lw=2,alpha=0.8)\n",
    "    \n",
    "    if clf is not None:\n",
    "        # Plot classifier decision boundary\n",
    "        xlim = ax.get_xlim()\n",
    "        ylim = ax.get_ylim()\n",
    "\n",
    "        xx = np.linspace(xlim[0], xlim[1], 100)\n",
    "        yy = np.linspace(ylim[0], ylim[1], 100)\n",
    "        XX, YY = np.meshgrid(xx, yy)\n",
    "        Z = clf.predict(np.c_[XX.ravel(), YY.ravel()])\n",
    "\n",
    "        # Put the result into a color plot\n",
    "        Z = Z.reshape(XX.shape)\n",
    "        ax.contourf(xx, yy, Z, cmap=cmap, alpha=.3, levels=1)\n",
    "        ax.contour(xx, yy, Z, colors='k', linestyles=':', linewidths=[2,0], levels=1,alpha=0.8)\n",
    "\n",
    "    plt.show()\n",
    "    \n",
    "labels = ['No','Yes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a model, we choose the well-known decision tree classifier. As well as training the model, this is a good time to initialise the MMD detector with the held-out reference data $\\mathbf{X}^{ref}$ by calling:\n",
    "\n",
    "```python\n",
    "detector = MMDDrift(X_ref, backend='pytorch', p_val=.05)\n",
    "```\n",
    "\n",
    "The significance threshold is set at $\\alpha=0.05$, meaning the detector will flag results as drift detected when the computed $p$-value is less than this i.e. $\\hat{p}< \\alpha$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean training accuracy 99.17%\n",
      "No GPU detected, fall back on CPU.\n"
     ]
    }
   ],
   "source": [
    "# Fit decision tree classifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier(max_depth=20)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "# Plot\n",
    "plot(X_ref,y_ref,true_slope,clf=clf)\n",
    "\n",
    "# Classifier accuracy\n",
    "print('Mean training accuracy %.2f%%' %(100*clf.score(X_ref,y_ref)))\n",
    "\n",
    "# Fit a drift detector to the training data\n",
    "from alibi_detect.cd import MMDDrift\n",
    "detector = MMDDrift(X_ref, backend='pytorch', p_val=.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No drift\n",
    "\n",
    "Before introducing drift, we first examine the case where no drift is present. We resample from the same mixture of Gaussian distributions to generate test data $\\mathbf{X}$. The individual data observations are different, but the underlying distributions are unchanged, hence no true drift is present. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean test accuracy 95.00%\n"
     ]
    }
   ],
   "source": [
    "N_test = 120\n",
    "X_0 = ref_norm_0.rvs(size=int(N_test*phi1),random_state=2)\n",
    "X_1 = ref_norm_1.rvs(size=int(N_test*phi2),random_state=2)\n",
    "X_test = np.vstack([X_0, X_1])\n",
    "\n",
    "# Plot\n",
    "y_test = true_model(X_test,true_slope)\n",
    "plot(X_test,y_test,true_slope,clf=clf)\n",
    "\n",
    "# Classifier accuracy\n",
    "print('Mean test accuracy %.2f%%' %(100*clf.score(X_test,y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, the model's mean test accuracy is relatively high. To run the detector on test data the `.predict()` is used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': {'is_drift': 0,\n",
       "  'distance': 0.0023595122654528344,\n",
       "  'p_val': 0.23999999463558197,\n",
       "  'threshold': 0.05,\n",
       "  'distance_threshold': 0.007951605},\n",
       " 'meta': {'name': 'MMDDriftTorch',\n",
       "  'detector_type': 'offline',\n",
       "  'data_type': None,\n",
       "  'backend': 'pytorch'}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detector.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the test statistic $S(\\mathbf{X})$, the MMD detector uses the\n",
    "[kernel trick](https://en.wikipedia.org/wiki/Kernel_method) to\n",
    "compute unbiased estimates of $\\text{MMD}^2$. The\n",
    "$\\text{MMD}$ is a distance-based measure between the two\n",
    "distributions $P$ and $P_{ref}$, based on the mean\n",
    "embeddings $\\mu$ and $\\mu_{ref}$ in a reproducing kernel\n",
    "Hilbert space $F$:\n",
    "\n",
    "$$\n",
    "\\text{MMD}^2\\left(F, P, P_{ref}\\right) = \\lVert \\mu - \\mu_{ref} \\rVert^2_{F}\n",
    "$$\n",
    "\n",
    "A $p$-value is then obtained via a [permutation\n",
    "test](<https://en.wikipedia.org/wiki/Resampling_(statistics)>) on the\n",
    "estimates of $\\text{MMD}^2$. As expected, since we are sampling from\n",
    "the reference distribution $P_{ref}(\\mathbf{X})$, the detector’s\n",
    "prediction is `'is_drift':0` here, indicating that drift is not\n",
    "detected. More specifically, the detector’s $p$-value (`p_val`)\n",
    "is above the threshold of $\\alpha=0.05$ (`threshold`),\n",
    "indicating that no statistically significant drift has been detected.\n",
    "The `.predict()` method also returns $\\hat{S}(\\mathbf{X})$\n",
    "(`distance_threshold`), which is the threshold in terms of the test\n",
    "statistic $S(\\mathbf{X})$ i.e. when\n",
    "$S(\\mathbf{X})\\ge \\hat{S}(\\mathbf{X})$ statistically significant\n",
    "drift has been detected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Covariate and prior drift \n",
    "\n",
    "To impose covariate drift, we apply a shift to the mean of one of the normal distributions:\n",
    "\n",
    "$$\n",
    "P(\\mathbf{X}) = \\phi_1 \\mathcal{N}\\left(\\left[-1\\color{red}{+3},-1\\color{red}{-3}\\right]^T, \\sigma^2\\mathbf{I} \\right) +\n",
    "\\phi_2 \\mathcal{N}\\left(\\left[1,1\\right]^T, \\sigma^2\\mathbf{I} \\right)\n",
    "$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o2o1Oq8Xnto94XdDnQKO5sFKUnA7jSnwEQdACHwLpwBWiKF70gT0ymWyQ8FQc3MsPv3n7OAnNENmwcRsxmcVDSsrI5Ericmazadsu/H7fObVhtISGhuJ19g7rmQ39O2yRkVE8/sgDfOfxh/ndb3/FnOl59NVtQPB0EWZQkZmeikrwUbf/Y+bNKiI8PILpRUV4rXX09ZiH3LOrtRoNDnJzh+5szpxegKVl+FUFj8uOr6+d7Ozxm55kvGy1IwiCEniHfl+fxaIoHhhjk8aE3ds24LT38e8//ZxLF1/DrXc/ikr15bPunUvsdjs9NidZR6t3nohWbyIo09HZ2UlcXPxZ67ers5PPN29hf2k5Xp+X+LhY5l9STGFhISfzkYmIiCQxLpzu1iqiEnOGtJvrSymeXjiwFiQIMh564H42bdrExi27sFmU2MQgWpXAdZfNYWbxDAAMRiP33HEzL73+DlZTGoaoFMRAgL7OGmTuDh66/y7kw6zrzJg+g+0799Jas5fY1IKBtR+X3UrzoU+4cvGlF1xyttNhXOx2Cf3L+W8C1wBXi6L46elcP552u06FKIp8tnYl77zyb/x+P/FJqXzj2/9NfFLqWJs2BIfdzi9//zey59w17FQGoHb3Ch6975azJj6NjY0888Jr6KInEJHYX8TP1tVCV0MJhbmJ3HLT9YCAx+OmrKyMmrpGFHI5E/JyyMnNoaOjg389+xKamAKiEvOQK5R43Q7M9aVovG1889EH0A3jABoI+Onu7kYmyIiIjBh2B6rPZmPXnj1UVTcgl8uYmJ9NYWEhWu3IlTJstl6Wr3iPqrpWtMZoAj43gt/OksvmMWtWMePB4XBcb7ULgvAv4GHg18CqE5qbTzX9upjE5xiNdVU8/ddf0tHWjFKl4vavP87chUtHfMnHBpE//uX/kEdPIzRyaEiFs89C1+E1PPWj752VPMeiGOS3f/gLmvhiTNHJg9qCAR+1JR9w502L0ev1/OflN0ETgy48iWDAj6O7FpPaz4P3343b7WbdJxs4WFGNTKFCJvqYPqWAyy5bgF4/clEAi6WbTZu3UrL/IB6Pl7i4GObNnsHUqVPOeDvcYumms7MTpVJJcnIyimGCei9Uxrv41AMjeVP9jyiKPz/Z9Rej+ED/wvPrL/yN7RvXYQg18cs/vYg+5MLKQVxaup+3PviM1MJrUKqPf8P7/V7q933MFfMKmHfJJWelr+rqKl56ey3p064ftr2rtQqdu5qOTgumjEsxnSCIbXX70Xkb+c7jj3BsdOR2u9Hr9Kd0ZGxtbeHp519BGZFLZEIuSrUWm6WVrrq9TMyIYtltN1/w/jjninGdRlUUxdSxtuFCRKPVcd+jT5I3sYhQU9gFJzwAkydPptvSw/oN76CLzEClD8frtuHsrKJ4av5ZLSnT1dWFMmTkkANDWCyVW9ZjSpw0RHgAYlMnU7u7htqaWtIzMlCrNaNcUxF57c0VGJJnERF33J/HFJmEMSyOQ3s/5MCBgxQUFHyZx7poGRfiI3FyTkxO/9HKV1EolBdIaIbAwgULmFI4mb1799HdYyU0ycCUW75OdPTIPixfBo1GQ9A7shuC1+3E7fESGpU2vKWCgDo8lZraGtIzMrBYumlubkEul5GWljbMWo9IdXU1H6/9lEOVtYTFCPi9bqISs5HJ+0dKMrmCsKRCtmzbJYnPCUjic5HR0d7C+2+/iBgMUnFgL/c99uQFEZoRFhbOokXnNpF+Tk4u3pWr8bodiDIF1h4rLo8XhVxGqNFId/MhIkynrpfu8Xj5z4uvcKS2Ga0pHoIBPLb3mTWjkKuWLjm6PiWyYuX7lBysQ25MJSI3DblCQWP9YVrrDjJp9nUoVP2jJkNYLM31W87ps49HxvprUeIsEx2bwKPf+wV6g5FDpbv5n/96gPIDJWNt1hnjcNg5fLiCysrDuEZwstRqtVw2fzbl21ZQVVmJzSUSlIfg8is5XLqN9pq9zJg2BVtn/bDXi6KIu7uekn0HaLHryJp1O8kTF5FccDlpM25j16FWVqx8H4CSkr3sLW8iveh6ohJzUWoM6MOTicm7HLTRVJcdd570epxoNOfPHSIYDFBZWcnWrVvYs2c3dvuFmXVGGvlchBwLzXju77/mSEUZf/n1f3Hl9Xdwzc33oFCMr//lPp+XDz5cTcn+Q6gM/dM0b5+ZGUUFXH3VkiG7PkmJifjcNhwNO1CFRCJTaPD0mQkxhhGSNoWubiu+3lZs3WkYIxIGXdvRcABZwIZfE0Na1oxBO4cKlYbUgsvYu/1NFszrZMOmbUSlTUeuUKLXyxH97QQDAWRyOWHJRbTsfQuP24Fao6e7uZy5RZNH9bx+n4/KI5XYbDYMBgM5OTmnlWiusbGBl19fjjuoRWWIQfS7WPHBOubNnsaVSy7nQtqaH19/iRKj5lhoxqoVL7Pq3VdZvfI1fF4Pt9796FibdhqIvPzqGzRaBNJmLhvwkvZ73ew7vBnbm8u5+87b+eILtXHzNlInX054TBq93S0EAz50hmKUKi1t9WVs3r6R1OQEKre/jSkum7C4bIKBAI6uGkIUbmKio/CE5OJ127FbOxBkcozhcSiUamRyJZrIDMoOHKCj00Jufr94yWQyoqMiMHd1oDVEIVMoUemjcPR2YG3vQ+FupXjmNad82rKyMla89xFoIpBrTAQ8NsQVq7jh2iVMmTJMsv4T6Orq5JkXXici81ISoo67Gvi9braUrEWpVHDZOZ76ng6S+FzEyGQyrr3l6+RMmMJbL/0fl19z21ibdFrU1tZS22wlY/oNg2qAKVQakicu5PCud2hubiYx8XiWw5q6RtKL5yJXKAmPSQXAbu2gZMObKEMTMWUuQhkRSoyxk+6GfURoPCTGxzFxxizy8/P52z+eoenIXux2K+qQGMSgH59zPfFpE0nJmYlCpcXt8SAIEAz4B5Loh4f3R9l3dplBpsRt76L54HqyM1JY9o170YeM7B8EUFVVxRvvfkzCxKXojREDx519Ft5+fw0ajfqUeao3bdmGNjofU9RgHyeFSkPSpMvYsOkdLpk754LxipbE5ytATv5knvrt08ezAAaDfLJ6BfMvv/aCDc0A2Lu/jJCY7CHFBwFkMjn66Gz27i8bJD6CIAyqKBEMBijf9TGhKcXoI1Nx2ToJMYVhSMokMiGblrLV3Pf1RYSFheP3+2huacajSiVh6uUDO1Z+r5OOIxvx+zajCLqIjysiLyeT9rYqYpKOC0J4eDgmkwlrtxm/NsAT336Y+PjBU7uR+Hj9Z0RmzBkkPAA6QzjRmXP5eN0G8vLyONm0qbSsnLjJNwzbptboUeijqa2tPefJ9keLtOD8FeGL6xerV77K8lf+xa9//AitTfVjZ9QpsDtcKNW6EduVaj12x+DF54l52XS31gz8bmmrBZUBfWQqYjBI0O9Bp+u/p84QgTYig127+rOyHDp0CKUhDkNMNoJw3ONaodIRnXsZbfUV+PpamTBhAosWXIKtqQRH76AEmwT8HrrrdnDdNVeMWnj6bDba2i2EnTBiOUZoVBKdPXZ6eoavhXYMr88/JID3i8gUKvx+/6hsOh9I4vMVZHLRbKLjEmltqudXP36YzZ9+dNJCfhZLN+Xlh6iqqsLvO5cR6IOJj43CZescsd1t6yA+ZnD+5kvnzaavdT8OWzcAfVYzmtBEEMFt7ybMZBxUqtoQmUJNfRMAu0pKiUwuICoiDGdvO163A4JBgsEgPq8LhT6KKQW5KJUqkpKSueu26+isWEP9/jU0HdlN46HPqdv1Ngtm5nHJ3Dmjfk6f34dcqRp2hAf9XxxyhRrfKT77pIQ4eruahm0LBgO4e9uJjR0+yHcskKZdX0GSUjN56jf/HgjNePmZP1JxcC93PvhddLrjaxO9vVaWr3ifmoY2NMYYgn4PoruHxYsuOfpyDc057HQ4kSvkaDQjB0uOlmlFU/l88zN4kicOKRrodtpwW2qZOvVKqqur2LJtFy1tZjRqNROykjhU9gFqUzJ9PRY8MhOOnlaMBh0x0dF4fV4cDidiMIjHYUdz9KV3OlyookMwhEWg1WrotvTgtPYgCGAwhKBMSMIYetyOvLx8fvqjTCoqKrB0W9Dq4pgw4bqBZPAejweXy4lOpzvp9NZoNCITvbidNjS6oV7qHpcdAi7Cwk7ur3XpJcW89u6nGCMTUSgG75CZ68tITYy+oJLOS+LzFeWLoRmvPf9ndm/bQG11BT/42Z+JiIzB5XLxr2deIGjIIHPW/IHAT7fTxseff4Lf52fBgvlA/xrStu3b2bhlJw6nh0DAT2pyAlcsupT0jOHTh46G8PAIrrnyUj5Y9yFhyUVExKYhitDdVoO1aS83XL2YLVu2s2XPIUITJhOWPRmn3UZp/UFEr5e5uVF0dMKeA7WkFV2CSqWita0dW58duVIHgozOIyX0aex0dXUSGxtFbY8ZQ1gMer0evX6wR3N9xwFiTvCOVipVFBQM3kbv6bGwZt2nHDh0BEGuQgx4mVKQx+WLFw5bUFChUDJnZhHbDuwiZdKiQVNkURRpq95N8fTCU265T5gwgeK6BnaWvIcxfhKGsDh8Hgc9rZVogt0se2jE3HtjwrgILD1TLtbA0rOFua2Zp//yC7Q6Pd976o/IZDI2bd7MZ7vrSZk4tCaWx+2gseQdnnryCbRaDa+/8TblDT3Eps9Eb4oiGAxgaa+jp34Xy268YsjLebpUV1exYeM2qmvrAcjOSmfhpXPweDy8vHwNaVOvRURGS0sbLq8PuUJNn7kKR/NObrnxahqbWuhwGxAMqTjcAdQh4YCAo7Mae8tekrOnEuwp55YbruGlNz8gbdqNQ9ZOHL2ddFT0R+CfTAQslm7+/q/nUYTnEp08AYVSjd/rxtx4EGzVPP7IA8MKkN/n4z8vvUpjl4fwhEnoDGG47FYszQeJC5Px4H13j9Lfpz/kY+v23bS2d6DRaJhZVMDUqVPOymj0yzCuo9rPFEl8To3P56G2ppp2cydiUGT9p59hylhAZMLwmfIayz7hussmo9VqeXl5fyT5iWkxnH0W2g5+xH//6ImztKt27G+1f2TwzPMv0itPISwmndq6elAaUGsN/c0iNJe8hcmgYdaULJqaW9hdVk1I3GREQYarp5GAx0Fs1kwSEpJoKP0YoxZ8Pj+9dhfJ+fOJTs4jGPDT3VqFrXkfdy277pQ7RS+9+gYtDiNxaUMFt6V6D5mRfpbdevOw1wYCfsrKyti6o4Qeay9hoUZmzSxi8uSCcZVC40TGdVS7xLnF4bDz5msvY21rJi1cA2KQIzs/wrnlQ2bd9BMScmYNuUZQafG4PewrPURo/MRh8/HoDOHIdTGUl5dTWHgqJzmRluYWLD391SHS0tKGye43eI2pqbmNpKI5dHZ14Q/K0CjVx08RQG1KwBQTxe59pWSnxaPUR+Jz9SICuoh0tKYk3K4e9mx4i5DIVFT6WBITE5A1VlC9dw2t5Z+gDzEwMT+XBQ/ddcoyzw6HncNHaskovmPY9pjkiZTteosbrnMP62sjlyuYMmUqU6ZMPcVndXEgic9XHpHXXnqByKCVK4r7q3K63G62RoZQ29LOlreeYtKCe8mdfdugEi4+eycRkVPp3LYbXfKkEe8u14ZhtVpPakFbWytvvL2S7l4PqpAIAl4H8oCda5YuZupJPHsDAT81NdW4Awpkcg0+awcymQydIQKFSo3o96BUaVGHJbN5yyaE8ImExE9GoTUiIMPvsdNTu5XwzAWoQyKRB/swhsdjDI8nLnUi5op1/PCJxzAYTp6qxO120dfXR19fHwp1yIDj4YkoVBpkSg0Oh+OCcfQbS74S4uO0Wfjolb+SnZ1HfFy/70VXVycHD5USERHJpImFA+d+vvETAOZferzA24GD++nu7mLihMlERvZv7ba2tXDkSAVxsfHk5PQPxd1uNzt2bkGtUjNr1vEEWSUlO+mz91E0dcbAH3J9fS31DbWkpqSTmtpfqqWvz0bJ3l0YQgwUFc0cuH779s14vB6KZ84dqFZQWVlOW3vrGT9TVFQcRmcrUzNjaW1ppLallejwMO6/9krWbtvJptJyDn32HK1lH3Plzd8hxBiOw9qJuruUw9sCxKtcYK8kRBlLl7mBLnMjkTHJRMb0535zOKqo2d2DzFrP3GvvGZLio6urk38/9zIhScVk5B4vSeywdbP8g3XIBOFo7uXBNDU1YrH0oJB3oIubgkyhAkEg6HXSZ+1AH2LEa2sjLPoyrF2tODw+lI4e1MajW81iEG93F+rQBNTGGIJeF4EvOCeGmGKwhKawd98+Lp136ZD+oT/F6eo16yk7WIlMpcPrstPd1UlkRivG8KFpYf1+LwGfG612ZN+lrxJfCT8fn9fJ2lVvouirYmayyMxkkbBgM2tXvUntgU0Dx2Ymi6xd9SZrV7056FjtgU2sXfUmYcHmgWOKvirWrnqTzrrdA8cmRLpYu+pNtmx4b9D1pTvXsnbVmyRpuweOucwHWLvqTVzmAwPHkrTdrF31JqU71w66fsuG91i76k0mRLoGjnXW7T4rz9R9ZAOzQhoo1DUisxzk822f09Wwl9kRXXxjmo5bJ4XjcfbS0VLF+uW/o2nX68jrV6LwtrHuozeZnaFC0baJOI0TZ9sByra9i7PtAPFaF5FCJ0LbFvbu+oyXXnqGveuXD/l/s2HjZtSR+UTGZw7a5dEbI4jPW8SqNZ8OW03io48/IXHCArzWBjy9zYhiEAGQq3TIlVpaDqwmPjUfhUqDzVyNQhsBoh9Xdx1iMIAoivgcnWhNyYAMn7tvSBUNQ0Qy1TXDl7zp67Pxj389T1WnnLSZy8iYcQs5c+/ElFbMvk0rhq1U0dl0mAm5mSfN2fxV4isx8tFpNFy/ZCFpScc9TuNioo4eG/wNdf2ShUOun1VUQGx0JHFfcGhLS0rg+iULmZhzfCtZo1Fx/ZKF6LWDh9QLZk9nQk4mJuPxXDJ5WWlcv2QheVnHt25NRgPXL1lIdMRgf44r58/B4XKj0Rzf7ZgyMRelUnnGz6QJOFHSn3IhKdLIFYVp5MT3xynFx8UxPcfN9qY+zDYX9p42VGIS3370UT7dvoeO7h6Kpk6hqbGJxvKNREZEMblwFrGxSTh6O2k9/DlXLJrLBIuNr12/lMM9g3PpiGKQvfsPkT7z9iH2QX+tLHNAQXNzM8nJx71/+2w2GprbyZy9CGN4PId2rsbavB+tKYmg3427p4Ggx0Zc+m30mOvB14suPB11WBo9dVvR2trQRWURCHgRPH14HV0gQCAg4nQ6BzygAwEfCsXwuaU/+2wjfl0ySVnTB47J5EqyJszE6wtwaMcqZlx+DzK5gmDAR0fTYTwdB1hy031D7uXzeXG73eh0umGrWAw8d5+Nbdt2sGtvGS6Xi+ioKObNmU5h4ZQLIGnc6fOV2O2aMiFX/OytZ8fajAuSPSUllH32AZdPTh22fXNFI3GTL2XepZeycfse5s2cOuAh7PP5USoVeDweVq9Zz+69ZSi0JgJ+LxpFkCsWzWP69GkDa8A7GwX0yRMG7u33+/jxz/6XvEvvH9G+xrI13HHdPLKyju+6mc1m/vHcG6RP7981EoNBao+U0d3ZjkprxBCThfngR8QmpiA4W5g2OY+PttaiiZqAoNTh6mnE1dOA32VDkCuInngtfpcVlUpFqEFDUkK/oNfvX8P1i4cuAAeDAZ76n9+SVHQzas3QShYul4t965/BGKJHb4zA5+4jKyOJa5cuITLq+BdYV1cn6z7ZQNmhSmRyJTIhyIyiyVy2cP6QrIkWSzf/9/R/COpSiErKR6nRYe9pp7txPznJYXzt9tsuWAGSdrskhmXSpEl8+vEqmrusJEaaBrV1Wh3UW31cPW0aMkFgwezj3/IWay/ffOo3fO36q1i6cC43XHc1S664jM7OThQKBbExMad8GRQKBSajAbu1gxDTUM/bYMCPu6+TiIjBwZahoUYCHgd+rxuFSoMgk5GRW0hMkoNuSw+OPjMeWxtTMwuYP+8q/H4/G7buIxj0EQx4CU0sJDSpCMQA5oMf0NeyD40pGZ0hHKezv/xwR1M5GmxMnDhxiF0ej4eAKAwrPNCf1Cw+OYNbls4hIiKCkJCQIYvWZrOZfz7zIuqoCWQU34FCqcbjsrOvZj+VVc/z2DfuHyRAby5fiTJyEjEpx+0xRSVjjEigct9qSkpKmD59OuOJC1MqJc4bapWKZXffy+c1VrZUNNFmsWHu6WNnZTNrKtq5/rY7MQyTDuKzrbto7+jmj8+8zK/+9ix2pxOtRkNyUhLxcXGDhGf5qnX87E//oqam6oS7CMybM4OO+r3DxpZ1NJaTkRJPePhg8dFotEyemEt7femg4yF6PSlJiRhkNq6/5nJuuO5awsLCiYqKpnBiNjJXAwR9uKwteO1deJ09yOQqLNUbsVSuprN6K7b2Smp2r0Rhq+QbD9wzrGOfWq1GIRPxuB3DfqbBYBCf00Z8fDxxcfHD7pat/GA1uvipxKUXDjg0qrUhJOfNxSlE8vnGzcc/hw4zzW0WopKG+hjJZHIiU6awaduuYW25kJHER4KU5GQe/fb3iZo4h5Iu2N0RQJ81jYcef4K8Ycr4Atx81WJ+9Nh9aDRqNmzbzUM//AUV1XXDnnu4pp7NO/di7R0alV1cPJOUSAV1+z7GZmklGPDhsltprNhKsKeCm24YPgnXVUsvR+luov7gRnrMDbjsVlx2Kw2HNoKthrSUJJqbmzjmmLjs1hvJjlVhrVqH6Oqgr3EbLTtfwGvvIjp7AaaEQuwd1WAt5/brF/DEdx4bInrHkMnkTJ9aQEd92bDt3a1VxEYaMRqH36K3WLppaDaP6MAZk1rIjt37BhbaOzs7URuiRxxJGsLjMZs7OJ3S0BcC0rRLAgCj0cCiRYtOK8n74nmzyMtK5xd/eZrq+iYe/+/f8sCyG7j16ssHvSg3Lb2MS4uLcGrSh9xDoVBy7z13snv3HjZv30nVwX4nw+JphcyZs3QgSPNEtFotE/OyWfPJRhqO7CPg9yGIXgx6DcbweD74dD8+Vy9GnZybrl9KRkYmj3zjfjQvvsxnm7fi8QaInnAV+sj0/oRhrj4ijPEY6GDXnn1MmDB0uvVFFi64lEP/eo7mKgWxKZNQqDT4fV5qKvbQWrGRyIhwnvqf31IwMZfFi+YPuGgA9Pb2otKbRiyUqNYZ8fqCeDwetFotarWaoM81oi0+jxO1Ws2FlCJ1NEjiI3FGJMbF8I9f/YhnX3+XFas/4dnX32XKxFxy0lMHzsnP6hednY3DvxxyuYLi4mKKi4tH1acoBnn1tbeobneRP+8utHoTTqeTqsNl9LUeIjY6h8TMqYiiSG93M8+/vJyH7l1GamoaEyfks3H7PkJj8tFHZQEgyARU+jDwO9DrIzhStwGzuZ2YmJHTTxgMRh5/+AE+Xrue/bveAoWGtpYmFGo9BfNuJSIuA7/PQ11TBX//1/M88uA9xMbGAaDX6/G5+hBFcdgKsz6PC5kM1Or+KV9qair4bLjsVrQhpiHndzVXMK1wZEfPCxVp2iVxxqiUSh675zZ+/V/f5ME7bhwkPOeCI0eOUNVkIbXgcrR6EwBd3Rb0kRnEF15HQ2UJPo8LQRAwRSYRnj6bD1evB0Q+37IDTUgUEYl5qJQyVAoZGrUKvV6HzhCB1WZHbUqhqqr6lHYYjEZuveUmfvaT7zFjcibxaROZddXDA4UDFUo1cemF6BOm8867Hw5cFx0dTXR4SL8bwDCYGw8ybcrxkBWFQsnSxZfSfHAdLod14DxRFOlqqcJnOcK8eaPPH3ShII18JM4as4omM+sLVRpKDpSz7+Bhpk7MpaO7h2BoLvrhk/WdFtt37cUYlz8wtQsEAjicLvRh4SAIaMKS6Wo5Qlx6vy3hMWlU79hBe3s73d29/WEOMiXKE0ogCzIZglyJ3ysSCIw+459arab0YCXxuUuGHclExmdRvbOEjg7z0UKJAtdfs4RnXnwTUQwSHpOGIJMRDPgwN1ZAbzULv/bgoHvMLJ5JEJGP136IoAlHptLh6eskKlTNIw/eTVhY+Gl+imOPJD4S5wSvz8fv/vkCXRYrz7+5Eo/Hw+VX3sYcUxJpaWmcyfpEj9WGNiZn4PdgMNhfB/3oi6/QmAbtRAkyGQqVDo/HQ1AMYgyLwdnTiNowdEFZFEW8vU0kJY1+29rv9+NwukjSm7B2NOKy9yBTKomISR9wBVDpw7FarQNVWlNT0/jGvbfz4UfrqN6xE6VGj9fZS3ZGMtc9fB8mk+mEXgRmFc9ietE0amprcLvdREREkJiYyHhb6zmGJD4S5wSVUskPH72X7zz1a9rNHcgFgZaqMj549TnkhnCW3XnPoEXY0eL1elAqZFgtHehDo4+mGJUDwYG6WX5XD+qw417efp8Hv9tGVFQUqckJdAdNdB7ZS0hkOkpd6MB5Qb8fR1c1qWFq0tOHLo6PhEKhIOj3sWv9S6DQoQqJJuhzUVO2lcSMAhKzp+Nz2dCf4DiYkpLKNx99CIulG6fTSagxFMMIO2QDfSmV5OQMvwM53pDER+KccaR0D3fOTOVQs4G9tWbKqw4zQ6chI1rHy889zSPf/t6o45z8fh9r133K9t37cHkCWHobsPs1REdFEhYWRlioEaujFwIerM1lBJ2dNNccwBQZh1yASROy0en0LF44j/+8vpLkrEIaD32ELiobbVgCQZ8PS2MJoQob9z3+XU5nNNHd3Y3T40UTP52whOO+OAGvi7aK9bidNkxaOQmJwyeUDw+PGHFb/2JGEh+Jc4LZbKaxuoJlxZksnZrButJ6nv68ml0HDmDp7SU1p4j9+/Yxa/bsU95LFIO8/Oqb1Hf6SSy8AZVax+E9a7A2H8DrzsQf8BMREUFz1Xo660sIS56KKakAUQxibavE2bafqy7pjx/LysrixqULWPnhOqJj47Db2+g2HyLosTGlIJcH7n/8FBn/RGpqathdsg9rr52oiDBsNhuxGbNwyU14nLajCc0E5CotpvRZNO9+lbu+9xjjdXp0rpDER+KccLiykrQwDXKZDKfHx6ycePyheazctIV506eh1hk5VFYyKvE5cqSK2hYraUXXDSwy50y7guaqElpqd3PwiI2YSBOOLjPpU5eCKhSf24FcLicluxDthEI++HgNubm5mEwmpk+fzoQJ+ZTuL6XTYsEYEkJBwaRTjj4CAT+vvv42Rxo6McTkotYnUtnRTfmuHeTMuYPU2Hja2ztw9LQgk6sQg36UCjnxKTknDRj9qiJ9IhLnBL/Ph0rRLxTPflrG9soWrl50Jd++6y5kMhlmqx2fz0fJjo3kFRQNqppxIrv27MMQmzfIcVEmk5OcM4PErCLqyj4j2eRDF55Aan5/EOiJPjS9ERns3r2HxYv7cxrpdPpRCZ/DbqeltQVBEKg4XElNm2tQylhTZBLVB7fR5wwQ4nCSnJSI3+/D6/Mhl8lRq9U0OqpPWfbmq4gkPhLnhLj4eI7s7n/htEo5IRoVMplsQECau23I5Xqe/ssvCI+K4aFv/ZT0rOHzI1t7+9CED18FQyaTozNG091TgSbsuFfyMeE5JkIh4YnUNZ4YWzYyXq+H9z9czd7SctTGGAJ+H/VHysie87X+nbUvYAiNIuB302XpwWQyoVAoB3IuB4MB3DYzsTExo+77q8K4cTIUBCFJEIR3BEHoFQTBJgjCu4IgnAWvEYlzQU5ODk7U1LVbeGhxIc89soQJGf25i2xODxVmB7PmzicpNZPujnZ+97NvseaDN4dNHBYVGYarr3vEvnwuC6EGPYGAF+jfeu/u7qaquoaKw0c4XFlFZ0c7iEPvPRyiGOTFl1/nUIOd9JnLSClYQmTqNHQR6Tg8Mlrb2gedH58+CUf7QbweL/4T/IM6Gg6Smhg9KJWGRD/jQnwEQdABnwG5wD3AXUAWsEEQhOHzGkiMKXKZjNvvuY9tDTa2VDRh7umjz+VhX20bH+5vZOHS65lYMIUnf/l3Llt6E8FAkBWvPcPffvsjbCcEoBbPKMLWVk7AP3Tq4nHa8PQ0smjRAlxd1QSDQRqbmumyOlHoowiJTEYTGkdHSzVVVVVYLCOL2DGqqqpoNNtJnjB/IOJcEGQIAmiNkdjsTjwez8D5UYk5hIVFYi5fS3npDg4eKuNg6R4ObPsAf/chbrtl+PrpX3XGhfgADwLpwPWiKL4niuL7wLVACvCNMbVMYkTi4+J45DvfJ3rSHHa0+zhgduOPzuaubzzO9Bn9OaqVShW33fMY3/yvX6M3GDlUupuf/+B+6qorBu6TmppK0aQ06vatpq/HjCiKiMEgPeZ6Gko/4rqll5GTk0NKXBgVu9fg9olojZH9idxFsLUdQq0QCUubzYqVH45k7gB79pYSEpszJK2r6HfjdfYiV+nptdkG2gRBQFSHEvT24bXU0de4G4f5EEGvHX8gMCQ9q0Q/42XN51pghyiKAwE3oijWCYKwFbgO+NOYWSZxUowGA/WdfZR3epkxL5eiy28Z9rzJRbP42e+e5bl//C9tLQ2ER35xjUTgphuuJzFuJ59t2kzbIReiGCQhLoqvL7tmwOnurq8t4zvf/xH+zhb0UZkIR+tzabVaJs2+FqVaR9W21ykvPwSATqcjJSVlyBqOw+FCrR5cJkcmV5CYWUBLzWbC0ubwxdlVV0cbrZXbKJh3M1EJOYOu62iq4PW3VvCtx76BtNU+mPEiPhOA94c5fggY/q9Z4oKhpb2DiqpaJkwbPvnWMcIiovjeU3+k09xKqKk/VikQCGCzWgiLiGJmcTEzi2dit9uRy+VDqkDI5TJMYREkFV5LR2M5XreDmJxCYpInIJPJ6Osx09HZxfOvvYcxPB6fuw+tws+N1ywhL//4YndcbBT7G8yYogcvKSZkTMXrdtKwbzmxKROQebrwOC00VOwiIW3yEOEBiErMpXpHKa2trcTHD+9k+FVlvIhPODA0ExVYgLBhjktcQNx763XcuGQR7YFT7/jIZDJi4o6POj569xU+Wb2Cex7+AUUz5wHCiDl+FAolfp+Hyr3rcbs9qHRh9FQdoLl6P3FpE2k8shd9wjQyJk0b8Ky2Wdp4+a0P+fodcnJy+sVjxvQitu56AV/SRJTq4w6HgiAQlzYJT9dh5k+Jw+v1ER6exEedR0ifNG9YmwRBQG2MobOzc9yJj8fjprS0lKqaBgQBJuRlMyF/Agrl2ameOl7E57QRBOEh4CHozzkjMXYkxsWQGBfDBGBzzT7qaquJioomPDwSAIfTQVNjPVqdjpTk49U8KioOUrLpY+zWLv752x+y4KpbufXuR0csvWyxdONyeVDGpZCYP7k/0FQEl62Nit3vEJ42E2NE4qCQDmN4HMGceXyweh0/yMkGBKKjY7hiwSzWbfqA8JRphMWkghikq7WG3qa93HbzdRRNPZ5UfsOm7fi8riH13Y8R9LlRqUZTZ/3Cobm5iedffB1RE4M+IgVRDFK5dg8frf2Mb9x/NxERkWfcx3hZcO5h+BHOSCMiRFF8RhTFaaIoTosMM51L2yROg57arbz43J+wN5cM1BAz+Rt46fk/s+OzdwbVFnvrtX9iMTfwvftuQa5QsOGjt/n1jx+htal+2Ht/+tkmYrJmozVG4fU4+7OKCqAOiQK5CkFlJCZ66EsTGpGItc9zNBVpP/PnX8rdty5F56mmctMLHNnyMtGKNh76+i2DhAdg2pRJdDdXDmuTx2kj4OoiI2N4P6ULEZfLxXMvvo4x9RJSCi4jMiGLqMQcUguXoIgo4Nn/vEIwGDjjfsbLyOcQ/es+J5IPlJ9nWyS+JC63h9LyI0ydlEd05PFQBoNez9RJeWSmJg06f8qEXLw+H9dfMZ+iSXl853+fprWpnl/9+GFu//rjzF24dGBHKhDws/9AOekzbycgCrS1mXFYe5EplPg9TmRyJWptyLAjEEEQUKj1uN2DU5Xm5uaRm5vXX5BQEBhpwXjunFns3vsMnS0mIuOzB2zyuOw0HljPkoVzRxytXYjs37cPQR8/ZM0LICopl7rOag4fPkx+/nCv5OgZL+LzAfD/BEFIF0WxFkAQhFRgDvDkWBomMXq0GjVPffuhIcez0pL5fz99Ysjx//3h4wP/fehIDbcvu5uSw0fYvnEdu7Z9xpwFVw686F6vDxEZCpUGBZCamozH68Hn9SEIEfTVaxEDHgKBod/YAb8Pr6OH8BEScp24G3YioaEmHnnwHl5/awU1O0v7PaJ9LgLOTpYsmMO8S+ae9PoLjfIjtYREpI7YrglPpqqq9isjPs8C3wTeFwThp/QPqH8JNAFPj6VhEueeHXvL+OvzrxGU63jqp79m4uTpZOdNHgjVCAaDaDRqNGrFoDzHapUa9dERR2xyNq3NZSiyhlaMMDccIDcr9ZS5dE5GTEwM3/3WIzQ3N/dXm1CryczMHFcjnmP06/lJKmGcpSIZ40J8RFF0CIKwEPgz8Ar9499Pge+IomgfU+MkzjlFk/KZN3MqhRNyCQkxMCHEwNZ1y1m3bhXFxXOpra0mJyefhRMSqGj4FDHvxiHpTFVqLdpgFy0VG4lOLURnjMTt7KWz8RAKVzPX3zFy1dTRI5CYmERiYtKpT72AycvOYN32KsJjh0+o5u5pIPuSS8+4n3EhPgCiKDYCN421HRLnH6VSwc+++/BRQen/2m0sc+F1WHB319JcV05zXTmJcdEcrDFT5HKiyF6KzhCBx2Wns/EQ2Gv5yZNPUHmkii3bNtDY24tWq2X2jKknLdFzIm63i8MVh3G6nISZwsjKzhoIIr1YKCwsZN1nm7GY6wmPSR3U1tFUTojCS07uUJ+m02XciI/EV5sTRzJLF17CJTOnotNqKT9Sw2/+7z9sKynD6/PR3VJKijKAw+2mrrYChULkjtvvJsTTTVFKOKq+GHbsqGZidgGz8hLA0khrnZW3l7+G0WjktlvvGuhn+TuvYbVauemm22lta2fv/kO0d3ZiNjcTH59CXFQU8+bORC4TWPXRSmJj47jm6hsHrv9ibfrxglar5YGv38HzL76OzXwEXVgyIkGcXXXoZE4evP/uEWuOnQ7jZatdQmIQep2W2KhIjCF6iqcW8Nzvf8aS+bMJDzVi7WogLz7AX35wIxPSjLh628iN9B7f2hc7qK3ch8LdPHAsP8pNbeU+OhrLB233dzZXUFu5D1/LLqp3r2Fi3iSMOhW27mYiTQbSk5PZs+ZldK4aaiv3YTMfGXS9o/HQWH9UX4qEhESe/MG3uXZBASkGK+mhdm69ajY/eOLxs5byVRiuRvbFxpQJueJnbz071mZInGOCwSCvrlzNy+98QDAo8sRDd5GSEIfV1sfEnEzCQvsXlJvbzdQ1thAfE0VGSv/6jNPlpuRAORq1mumTj49W9pSV43C6WL9uPTH5S9EbI+jqMmO1dhEREdMfzd5UTmigmUmFkzGG6Jmcn8Mv//oM1Q1NXHvLQyRNnkdvby86nY7QUNNYfDRjyrxJ4SWiKE478bg07ZK4aJDJZNx909UU5mez6tPNLF0wd9j65omxMSTGDvZ612k1XDJj6pBzpxXk09TcDHIdemP/N35kZAyRXwh8jYrPomrrDh687y6Uiv5Xqr2ji/rGFrbv2M2LH+9FqTHgcztISojimqWXk5QkpaKSpl0SFx0Fedn8+Jv3DwhPd4+Vf73yNh6v90vdz+fzIRshdAJAkCkA2SAfou/cfwcz89LxqmJJn7mMtGk3kjn7DhyqDP79n9doaKj/UrZcTEjiI3HR84enX2L5qvU88uNfU9/UetrXR0dF47V34/d5hm2395oJNepRq48LVEV5OfqoXKITcwdivmQyOZEJWYSnzeXdDz7mrDnMjFMk8ZG46Hlg2Y0kxkVT39TKwz/+FR99upnTWesMCdEzeWIObTUlQ64LBgN01Oxm/tyZA8EXQVFkV0kpMSmThr1fWEwqXT0OzGbzl32kiwJJfCQuejJTk/j3b57iivmz8Xp9/PGZl/nlX5/B7nSO+h7XXbMUo9BFfek6rF3NuBxWulqrqS35gNyUMIqLZw6c6/N6OVzfws49W7D3WYbcSxAElFojfX19Z+X5xivSgrPEVwKdVsMPH7mXqRPz+Mtzr/L59j1U1TXy3B9+hnoU6S50Wi2PPfwA+/fvZ/uufVibHERFhnPVDQvJy8tD9gU/JKVKRWtnN7bWDSxKnDLkJRODQbxOK6GhoQyH2dxOW2sbcoWczMzMIUnTLhYk8ZH4SrH4kmLyMtP45V+fYdbUglEJzzFUSiUzpk9nxvTpJz1PJghcvXAupTWd6A1hnLhS1N1WQ1xUKFFR0YOOW61WXn/zHZrNFjTGeIIBH97lHzJ3VhFLrrh82J278YwkPhJfORLjYvj7L59E/oWXubyqlrjoyAFfoDPl2w/fyz/++SxtPW0EDDmo1DoCfh9dzZU42vZxxwN3DTrf7Xbxr2f+A6ZcMmdejnDUNp/Xxbb9nxIIfMw1V191Vmy7ULi4pFRCYpSolErk8v4QAYu1l5/+4R/c/4Ofs6fs7KSH0ut0PPbIA6REKWncs5zqHW9Svf01YjXdPPaNr5OQMDhBfUnJXjzySGJTCwaEB0Cp0pJSsJjtu0rp+0LFjIsBaeQj8ZVHFEVSEuIoLT/CD//3Lyy79gruvfU6FIov/3pU1zfhdLmYWjiJ+Td8DbvdjkajHZTC9Yvs2XeQsPjCYdsUSjXqsBQqDlcwY8bMYc8Zj0gjH4mvPBFhJv741Pf4+q3XIQjwxvtr+PbP/0BbR9eXvuefn32F7/z8D7S1t6JUqggLCx9ReABcbjcK1cjtMoVmUKHCiwFJfCQkOB6a8af//j6R4SYqqmp56Ie/YNue/V/qfhmpSUzMzUSjHllQvkhifCx9Pe0jtnsdZqKjo0dsH49I4iMh8QUK8rJ57vc/Y/a0yThdbjSaL5eJ8IkH7+Jv//ND4uLiR3X+nFnTsbWU4fe6h7RZOxrR4CJrmCyM4xlpzUdC4gSMhhB++f3HqKiqJT/7eNUJW58doyHknPSZlpbGvJkFbNr9AWGJUzBFJeH3eehuO4Knq5KH7r3jottqv7ieRkLiLCEIwiDhKTlQzrJvPsmqTzedVmjGafTIkiWLuefWqzAFG2koeZuO8o8oSFLy3W8+eFFGwUsjHwmJUbCnrBy328OfnnmFvQcqeOKhuwjRjex5/P1f/YnKmnruuv8J8kedzVAgJydnoHLqxc5pjXwEQcgQBGGDIAi1giD8SRAEzRfadp198yQkLgy+8bWb+dE370erUfP59j089MNfUF5VO+L5Lpcbh9NFMBg8j1aOL04rk6EgCGuAj4BtwHeANGCJKIp2QRD2iaI45ZxYeYZImQwlzhbNbWZ++ddnqKprRCaX8cCyG7j16qGhD06Xm0AwyIEODca0gjGy9sJgpEyGp7vmEyOK4t9FUSwRRfEuYD2wXhAEA1/15CQSXwmOhWbctPQygoEgyz9aj90xNDpep9Vg0OsGvKglhnK6az6DnBZEUfwfQRACwDrg3GwDSEhcYKiUSh675zaKJuWhUinP2Q7Yxc7pik+VIAgLRVH87NgBURR/JQiCHPjZ2TVNQuLCpnjq4OnUi8s/wOfzce+t1/Hqux/RYu5k8uxrx2X5nPPBKcVHEISpoijuPfrrXQwzvTo6Alp+to2TkBgvdHRZeO291QT8AfaXH8HpctHQ3EbG5IVjbdoFy2jWfDYIgrAAQBRFqyiKvcOdJIri2QkHlpAYh0RHhvPHnz4xEJpR29jC1YvmERlxcYVEnE1GIz6vA6sFQRhSqlgQhLmCIGw5+2ZJSIw/vhiaIZfJWPXpJj5avRKv9+IKCD1bnFJ8RFF8BPgN8KYgCA8DCIIwURCED4FNQNi5NVFCYvxwLDTj8XtvR6FQsG3bJt5++V9jbdYFyai22kVR/AXwCPA3QRA2AvuBicB9wPAp+iUkvqIIgkByQix333w1ySlpXHPz3WNt0gXJqMRHEIQwIAsIAJcAO4AsURRfFEVRcuGUkDiB/7z5Hv958z1uunEZoaZwAAKBAB++8xJOx1e7asUxTik+giD8HKgDHgP+SP9oZxrwp3NqmYTEOGbqpDzmz5qGXnfcB+jj91/ng+Uv8Ysnv0FtlbQ/Mxo/nx8DzwG/EEWxHUAQhCbgXUEQYoA7RVH0nUMbJSTGHfcvuwGAnY3HS+pMn7WA/bu30lB7hN/97FvcsOwBLr/61osuVcZoGc1T54mi+Ogx4QEQRfFTYAFwKbDmXBknIXExEROXyJO/+DuLr7qZYCDIitee4a+/eRJbb89YmzYmjGa3q2aE43uBuUDqWbZJQmLc4/X5cHs8Q6LaFUolt979KA8/8XOCCGz5fC2P3LmUdWs/uuhyNJ+KMxrviaJYDcw+S7aMiCAI2YIg/FUQhDJBEOyCILQJgvCBIAiTz3XfEhJfhu/+/A8svfubNDTWDWkzm9v5/LONFE2YQnJUBLFGHS1lO/nb//stbW2tY2Dt2HDGycREUTwf1e4vp3+a9xKwFzAB/wXsEARhriiKJefBBgmJUaNUKlGplMiEwd/vfp+PV194lmlxajLj8wnOyMXj9aLVaKhtt/D8P//KfQ8/SnxCyhhZfv4YLytdbwKTRVH8oyiKG0RRXAksAdzAt8fWNAmJofzl5z9gzSv/JCUlbdDxg4cOYpL5yIyPBPqrZmg1/Tn5UqNN1Jdv4yffvoc92z8/3yafd8aF+Iii2CWekPXsaIzZESBhbKySkDh9aqoqSYkYPv2qz+8n0qDH1tvD03/5Ba88+6eLOjRjXIjPcAiCEE6/l3XFWNsiITFaZIKAOELePbVKxeJL5lNUvAiFQsGmT1bx6x89TGtT/Wn14fN5aWtrpauzkws5x994TiD/d0AA/jLGdkhIDOFv/3mdhpY2Lrn8DjK/kM8nIzuPnWsOkZ90/NxAMIhA/xSsvtvJFdd/jeiIUJ7+6y9obW7gVz9+mNu//jiXLLrqpH36fF7Wrv+UHbv3I1PqCfh9GHQKllx2KYWFhefmQc+AMREfQRAuoz8F66nYKIri/GGu/xFwB3D/0R234fp4CHgI+lNfSkicTypr6qmormP6pa5Bx/Pz8/h0jZrDzV3EGZRYurvwe72IQIcrSKtDzd0Fk1AqVfz0f//NGy/+nW2fr8Xac/LSzcFggBdeeo0Wq4zEwhtQa0MQRZG+nnbefv9TnE4ns2ef843p0+K0EsiftU4FQQeMphCRUxTFxhOufRj4F/BTURR/PZr+pATyEuebwzX1OJxO+lRpROZMH9TW2dnBH3/7K0ICdibEGVArFNRbnDT3etAawlh68zKKio5fc2D/TiYUTB/whPZ43KjVmkH3PHCgjLdWbSV96tUIgjCozePqo3Hvuzz15BMnrRd/rhgpgfyYjHxEUXQCh0/3OkEQ7gL+CfxxtMIjITEW5GakAoPDK47hdrsJN2iZHB9Lm6UXfJCdnsLS+Egcbg/vf7CSvLx8dDo9AJMKZw5ca+3p5tc/ephFS28aFJqxY9deTPH5Q4QHQK01oDIkUl5+iKKiIRowZoybNR9BEG4AXgCeE0Xx+2Ntj4TEl2XPrh1MjDMyMTWWiamxg9pMei3JJhVlZWUUF88acu2BvTuw9nSz4rVnqDiwl/u/+SOMoWH09PahSzaN2KdMbaSv78KKph8Xu12CIMwD3gBKgRcFQSj+ws8FWStM4qvNZ9t28c5H6+nttQ5ps3R2EGnUj3hthE5JT/fwazyXLLqKbz35v4QYQikv28PPf3A/5WV7iAw34eqzjHjPoMeKKdR0uo9xThkX4gMsBNTAVGArsP0LPyvH0C4JiWFZ8dEn/PPlt7H0dA9p04cY6HO6R7zW7vajDxm5HM+kKcX87PfPkjOhkL5eK3/53x/ittTT01xKMBgYcr7LbsVvbyMvP//LPcw5YlyIjyiKPxdFURjhJ3Ws7ZOQOJEFs6dz49JFGA2hQ9oKp82gor2PYHDoZo/H56e628WkgpOHLZrCI3nip/+P6267FwSBfbs2EKX3U1e6DufREZAYDGIx19NUtpobr12CWq0+Ow93lhg3az4SEuOJm69aDAy/4JydncOO2BQ+P9RAcXYCOrUSAKvDxcbyFgpnziEsLPyUfchkMq6+8S5y8gtpqD3CwiXXs2nTZjZuXYPHD8GAn4TYSO694zqys3PO7gOeBSTxkZA4z8hkMr5299dZu3o1y/fsIlynIBgUsfsFiuctZt6l80/rflm5k8jK7U+lPn/+fCJMWnZs/oSb7/gGYRGR5+AJzg6S+EhclPh8fg4eOkjlwTICAT+JaZkUTS0iJGTkhd6zSUeXBa/fh88XMWy7Uqni6uuuZ9HlV2A2tyOTyYiPj0ehUJ5Rv36/n9ee+wvdnWaa66v5xnd+RnxS6hnd81wxLtZ8JCROhggEv+Asa+np4R9/+X+UrFtJjM9MstBDy94N/P2Pv6GqeliH+LPO//z539z97Z/S3NJ40vO0Wi2pqWkkJ6ecsfAAKBQKHvv+L4mJT6S1uYFf/ugbbPzkQ8bCmfhUSCMfiXFLV3c3mzd+zsH9Jfh9PiIio5g+Zx67t28mzwQTU1IHzk2LDcfc08c7r73II9/5AabQoQvBZ5PIiDDi++woz4KgnC5JqZk89ZuneeOFv7P18zW8+uyfqTiwl7sfegKd3nDe7RmJMQmvON9I4RUXHy2trbzy/NPkRarIT4xCo1LQ3tPHJwca8Lhc3H/F9GG9fbcdbiY8r5jLFi8edV8iUFpays4tG+lob0OpUjFpShGz5lxCeNjJa2bubBTQfyGw9HyzY/N6Xn3uL3jcLjKy8/nhL/4+7OdyLhkpvEKadkmMO0Tg3bfeYFaygaKMeLRqJYIgEBduJD3SQKIeent7h702JcpIXdXoI3tEYOU7b7N51dtMCg1w56wMrp0Uh7ehjGf/8Rda29rOzkOdI4ovWcxTv3ua1MxcrrvtvvMuPCdDEh+JcUdTYyN+ew/psUO3o2Uy0Krk9FqH9/YVRRFBNvoX8ODBg7RUlnL11HSSo00oFXKMOjXTsxKYmRTCijdfu4Az5vQTE5vIj375D/ImTh04tn3T+jGvmiGJj8S4o7vbQlSIathv8aSoMBqtXrwjVIKo7eglK3/0Fb53bd1EQWI4SvnQVyUjLgK/vYeGhoYhbb/86zPc88RTp1xwPh38fh9lZaWsXPE2761YzsGDBwgE/KO69ou1wSoO7uWFf/52IDRjrJAWnCXGHRqtBqdvaBgBQEKEEUGhpNJsJzNbHCRQjR1WmvqCXDO1aNR9dZjbmTclcdg2QRCIMarp6uoiNWVwwvf2ji6aWtrx+c5OPU2z2cxrLzyLUeYlJbw/LcaWVaW884aP+IREFHIZcYnJTJ9ZTGRk1EnvFRefTHb+ZCoP7efPv/4vlly7jOtuuw+F4vzKgSQ+EuOOzIxM3nOJWB0uTPrB+WkEQSAuKpwai4/3SmrJiNChkMtotLiw+GTcce+DGE4SN3Uiao0Gp8eLVj38rpXLFxw2bOGn334Ir9dLo+fkQjAafD4vr77wDEWxarLi4/uP+f1o/XaMPgflh/dz2ZQc6g5u5++bPueqG25hRnHxiPc7Fpqx+r3X+GD5S6z54E2OVJTy4Ld+SmR03BnbO1qkaZfEuEOpVLDgiqtYd6AZS59z4HggGKSsvp0uv4bvP/kTltxyD4HYCTjDs5hy2XV8579+TFLi8KOYkSiYOp2KlqHBoQB2l4f2Ph9ZWVlD2uKiI0lJjEelVJ3eww1DWdkBwhU+suKPeyu3NTejlQWZlhqJXh7kwx0HsfZY0PptvPzs/7F2zcecmL/Z4bCzZfNG3nr9Zd5/dzmZ+dP43lN/JCwiitqqCn7xw4eoPLR/WBsCAT99NttZLWwojXwkxiXFxTORy2V8vHY1IYoAWoWcTruH6IQU7v3GrRiNBoxGw7DCcDrMLJ7F03t2crDBTH5SNLKji9U2p4f1BxqZu+ByNOcgYDMYDNDY2IjX6+VgaQmpEcdHa263G5/XRaRJT3tPHykmFWE6uCy/f9TS0Glj09r30Ot1zL3kUgAqKsp5/+3XSQ5VkmDS4nUG+OzdA8gMkXz/Z39h+Sv/pObIIeISB08f3W4Xn372OTt27ycgygj6veTlZrJk8QJiYgbnIjpdJD8fiXGNP9D/kno8HiIjI4mKPPuxTN0WC+++/QZWcwsxRjVuX5BuV4C5Cy9n7iXzGG7vbOWaz+jotpA8YQFJhZeeVn/79pbw6ZpVaPChVck5WNPM7DQT0ydko1Qq6bH24OrpRKmQYXc4sTr9tDsCzM/tFwN/IEBDj4sSi5LvPvkUDoed5/7vzyzJjyMq9Hh4iSiK7K5uxaqO5uv3P4Slu4OIyP5854FAgNbmOlZ8sI5ev4mY9CmotQYCfh9dLZU42vbz8P13kpBw6pHkBZVGVULibKGQy0lPSzv1iWdARHg4Dz78GO1mMx0dHSiVSjIyMlApR/Ze/mTzDiqq63goaSpJI541lD27d7Fx9UoWT4gfSDgWo1dQU99IY0M9KalpCAgERRG704NBraS0pY+0aNPAPUQRdColiaEqyssPYW5rITdKO0h4oH99bFpGPG/urKa9vY24uPiBtlUrXuatV57GlDaHaVd9Z2DhXq5QEpMykS6lhuXvfsh3Hn8YhpXfUyOJj8RFTyAY5PDhw+zdtQNHXy+m8EiKZs4iMzPztF6b2JgYYmNGVwnl+isWcMnMqaNKjXEMv9/HJx+v4upJiYSFHF9Iz0+OYV9VE902J6FWK8ZQI53mIEIwSLPVg8XlZ1HUcWFxeHyEhBgx+L04HA6qD1cwP214T2yZTCDVpKG+rn5AfERRxNrTRW+vDcfhrQgITL7sQZSa431ExGVQ3bCH9vZ2YmO/3CK1tOAscVHj9fl4+YXn+fy910kUu5keoyDc3cLqN15gxdtvDQpIPZssnjeLZdcuwRR68vCLL1JTU0O4hkHCA6BWKlg6cwL7Wx18XFJFQ6eNbg9squ5mc20Pl09IQCGXA+D2+nB4A5jCwrC4A5hMpv6bnOQ5RRg0eBEEgbsefIKE7BmotCG0Vu1g42tPYmk7MugctT5sRE/y0SCJj8RFzfq1a5D1NHPN1HSyEiKJMYWQnxTDtdPS6ao5wI7t28faxAFcLhc6pXzYttgwA7cvLKIroKZdEYMnJh9faCKCTI5MDNDndNNpc9Lt9BGfmEyf20enUyQvL4/M3HxqO6zD3jcYFGmwukk7Yeoqk8mIScqm+MafYIpJx2nrZOvbP6dq9/uIwSCiKOJz2dDrv3yKEkl8JC5aPB4P+3fvoDg7bmCX6hhKuYzpGTHs2Pz5OQmPqK5vYnfpoaMjg9H1EB4eTpfdO2L6i16ni8yMdJbdeQ933HUv//PLX5E5ZTYbG11YAipMUbGkpGXSYnWy5mALV914C0qlipmzZnO4043Zah90P1EU2VXVQnRi2jBTJ4FZ06dit3Yy97b/IaPoKsRgkK7GA/22dDdj1MpIPE3XhS8irflIXLR0dnZhVMvRa4bfCo8xheBxtGK320/L8fBUVFVV8eiPf0lTWyezZl+BMnoTs+YtYPqMGZxscTY5ORm5PpTa9h4y4gavFQWCQfY1WJi2+LqBY4Ig494HHmL7tq3s2rqRXa1mAsE2ElLTueWem0lLSwcgIiKSG2+/m3ffeIVEo4UEk64/V3SnE1VYNHfe/rVh7Zk3bw77Dz5Le72a3NnLiEyahDEqmZ7ORrprNnPPshtO+jynQhIfiYsWmVyGLxAcsT0YDBIQxYH1krNBSckePv1wBbmRGpKN8czJTUSm07Jr/fu0tbZw7fX9L2x3dxd7du6ktbkelVrDhMlTmThxIjfedgevPv80NpeHvMQo1Eo5ZqudvXWdGOIzmDp16qD+ZDIZc+Zewpy5c3E4HEdDJISjVSxEjolDdnYO3/rBj9i/bx+tTY0oQ1VcsaCgf9FdGH4CFBJi4JsP388Hqz7m0I7XUelM2NrLiQ43cN+dN7PytX+Snpn3pUMzJPGRuGiJjYkhqFDT2esYss0MUN9hJTYh6ayVEHa6XKz5YCXXFSZh0mcDUOoKQ6E1sNSoY+WePdQXTqGrs5NPVq0kJ1pHflgIHp+T3eveZcuGT7nn/oe4/9FvsXnjBl7fuZdgIIApPJwZ85Yys3gmMtlIQilgNpvZ+Nl6mutrkQsC+lATxXPnM2PmTARBhk6nZ/acuaf1TEZjKHfesQy7vQ+r1YpWqyUiIoLK8jKqDh/gSHkpleWlPPTt0w/NkJwMJS5qdu3cybZ173NVYeqg+Cyrw8Xq0iau/9p9ZJ+hF/QxduzcSdXWj1kw8biXcKkrGYW2P3vgwfp2WmURdDbXc01hEkbd4HrrJTVtdMnDue+hR44eEQkEAsjlpx4jlJWVsubdN5mRGkZ6bDgyQcBstbOrpoOYrAJuvOVWzmSKNBxVhw/w7N9+RU93Jxqtjrsf+h7TZy8Ycp6UTEziK8n0mTOZPPcylu+u5/NDjeytbuHTsgbe39/ComtuOmvCA2C1dBOmG9nxMMKop/bIYSbFhQwRHoApabFY2hppa2s9ekQYlfB4PB4+evdtrpyUQFZ8JHKZDEEQiA0zcOWUNJoOl1JVdfZzV2flTuJnv3+WwulzcLucPPPXX/LyM3/E4xm5IOIXkaZdEhc1ArBgwUKmTZ/BgQMHsNvt5IeFcevEiWg1QwXgTNCHGGjx9OfX+dWK7dSardx81Y2kpPSPfGxON06nk9SYkR3+Ek0ampqaiI2Npba2lvb2dlQqFTk5ORiNg/NO+30+SkpK+HTtKjpbGlnn6SUnJZaJyTEoFf3TM6VcxoR4IyU7t51xnNvwz2zk0e/9gg1r32P5q/9m86cfkZiczsIlN5zyWkl8JL4SGEJCmD1r1jntY1JBAZvXr2aa14/d6cLa56CzowOZICPEaKK8rRdDaBiBYSqVHiMQFLFarfztj79H7rETZ1Ti8Yt88uG7TCoqZuk11yCTyfF6Pbz0/LPI+tqZFi1HHxWLiIyDzS1UNZm5bvYk1Mr+1zvSqOdIa+c5e25BEFi45Aaycifx+foPmH/5dae+CEl8JCROiQi0trbS0tKCXC4nKzMLo3FoFQhTaChTZ13C6598yI0TwomdHU+TKgKHz8va3eWY/VpmzZ1HVdthZmQlDLneFwhS1+WgdstGLs0IIyMufaDN6/fzSdkeVgsCV197HZ99sh6ts4P5k9Ow9lpxWNxEGHTEGLVsq+liR0UDlxZkAP0jLv0wZZvPNkmpmdz14BM4nQ7Kyspoaajl4P5tI54viY+ExEmw9vay/I3XsJqbSTRp8AdF1r7nIa9wKldfez3KE7aYjaGhiDI5B80uai0eug19VHW7yU6MJkZQ4Ha5aOhykxTRR1z4cQELBINsqWhCqTOSbgiSETe42KBKoWDRpGTe3LmdOZdcwv7dO7ihMAFBEDCEGOgytxMIBpHLZExJNrGytJ1Z+Sko5XLKW3uZc/UVo3re3l4rBw4cwGHvIzwikkmTJqHRjH43sGT3btauWkmyScm+km14/cNnnARJfCQkRsTj8fDis/8iyyCypDhzILLb5w+w4VAZ77/r5+Zblw2cLwI7Nm/kmuk5RBh1dPU6KPclMGtqBDanh5KqJvZt+ISYuHhe33KY9JhQMqINuP1BarucxKZmo2pvJTvOREu3jW6bA6VCTkp0GDq1ErVSQZJJQ2lpKSpZkBBtv/OkQqEgLCKKzp4uIkI06FRKtEo5HVYH1WYryoh48vPzT/G0IuvXrGHPto2kRWgJUcspP+hj/ar3WHr9LRROmXLKz6uqqorPVr/L9YVJhOo1FCSEolapePCp/cOeL4mPhMQI7C8tJRQXk9NSBx1XKuQsnJjCmztK6ey6bCCHkNPpxO2wEW2KY/n2w5itTvILE2no6GFLWRUTYkO4ZmIUkTFRtFj17K7rQhuXTnJ6CrffMJH4+Hh+9d8/5eNdzcjEALFGFR6/yJayKvJTE5iVl4JK3i+AHl9gYKQDEBkZiVwup7O7EyEYwNzrYs3BVqZML+aWq64+5a7Zli2bqSzZzK0z0tGojp/bY3ex+v23MBqNpGdknPQemz5dx8zUcEL1/Qv5EceCWkdAEh8JiRE4tL+E3FjTsG0KuYz0cA3l5eVcOm/e0WNyAsH+KVRpfSfV7T3EpdvZUtPI0vxowvRq2q0OTCFaYiPDSIk2sfpgA7ff8TV0Oj19fTZ6LF3MSVQzKTFmYKTl9vn59LCZ7Yi02gPMSk+nqiKRerN1UBhGWFgYJpOJqmYzCZkmHnn8u2i1ulM+p9/vY9uGT1g6IWGQ8EB/hP2MlHA2f/7JScXH4/HQ2tzA4rnZp+zvGJKfj4TECHg9btSqkb+f1UoZPp/3+O9qNUlp6dS2Wbi5OJvHlkzB6Q+SFq4hTK/G5/cTFOQDCecjjXqSQ5Xs27sXgF07djAlJYLIEPWg4FKNUsGi3BhKjjQi15tITk5m0RVL2VHXPSRYtKvXye6mPq654dZRCQ9Aa2sbOnlwSCqPY6THhdNYVzPoWU8kGAwiEwRkp1GUUBr5SFzQBEWRmpoauru70Wq1ZGdnn3X/nJGITUimtaOSGNPwQaftfT7mnJDH+NJFl/P2i8+wZGI8EUY9VaUQF6olEAzQ1ecmIjpuUDmfeJOOtpYmAA7u38OCtDhEr4t2SydGTf86T1AM4nD7CNOpmFzUH5yanp7BNbfexaqVyzHIOzFpFfS6A/T6ZSy9+Wun5dMTDAZQDFOX7BgyQUCgX2BGQqvVYDSF09bTR3y4cVT9SuIjccHS0NjIijdfRel3EqVX4fIFWbXCx9xFlzNv3qVDggXcHg9WqxWVSkVYWNgZBxPMmDWbV58uITveMyQyvqnLSm9ASVZWFjt27GT39k1YurvR6XTEpGax6kA1MXoFva443G5QygTCo6IxmUw4HA6cTgeIYLU5UIb2V7jwer2oVUpCjHp0eh1WSw99ThcyQYYhNJLkRA063fHRTF5+Pjm5P6W6uhqbzYbBYCQzM+Po+o5Id3c3LpcLk8lESMhQ14BjxMTEYnH4cXl9aFVDPbRbLX2YIiKHLRF0HIFZly5kx9qVXG3UDzg5noxxKT6CICwD3gBaRFH88glFJC5YOjo7ef2FZ7g0I4LkLwQs2l0e1m5ci1KhYPbsOUB/Eq61a1ZzaH8JISo5bp8fY0Q0ly25+oy8euPj4ph92ZW8/+nHFCYYSY4y4QsEqWrrprLLy6133csbr72Ct6Oe4tQoInMyMXdbONhYTl27DfWkqYSGp1Nd18icjExEoL6uFtHvRauUAyIlR9pR98Al8xcQG59Ia7eF7IRIdFoduoTjQiOKIubqHuaekMZVJpOTnZ0z6FhdXS1rV32AzWImRK3E6vSSnp3HlddcR2ioachzarVaJk4pYseRMuZPSB40MvP5A+yq7aD4ilOnz5g2bRrtrS2s2L2L/Fg9EUY9fc6RQy3GXWCpIAgm4DD9O5uB0YiPFFg6/nh3xXKUnVVMSR8aKW11uFh10Mz3fvQUwUCA5/79TyKwUZQeh1atRBRFGjqsbK3p5Opb72LCKbeZT05dfT07t26mqb4WuUJB3qRCZhTPovzQQSq3r+eKyWm4XC7aWppRCCJqhcBPPjhETZeL2+9+HCshhPt7iFF60MtFQjRKgmKQPQ1WLD45+SmxHLSIXH7Vtaxb8RrXFaUNeCcfo7KliwqbnMe+/QQnE4GammreeeU/zEkPJzUmDEEQ8PkDHGgwU9ULDz72LQyGodMir9fDqy++gLurmfw4Iwadmi6bk/K2PjImT+fa664/ab/HEWlsbKRk104sXR3oQwz883ffvWiqV/weKAXagMvG2BaJs4TP76e8vJz6mipkMjk7t23lvktzhz3XpNcSqoSGhgba29rQ+6zMmZQy8I0tCAKpMWFo1UpWv/cOubk/HdiS/jKkpaaSlpo66JhIfx33hRkxeH1eWpsbidSrBhao52VHE6QTa3sTyTOXUnXEw7ayI+RFawkGgzT2uNGrFBSlR6GRQ3drA++8/jIej4d/r9nD3LwkchKj8Hh9HG6x0GCHex48VaUIkdXvv8u8rEiSo0wDR5UKOVMz4nEfbmLLpo1cedU1Q65UqdR8/YEHOXz4MPt278TR1Ud4VCo3LC4mNTX1FP1+EYHk5BSSk49H9v/zd98d9sxxJT6CIMwB7gQKgJ+OsTkSZ4m29vb+OuRyP8kmLUExiNxl5f2tB7l61kSMuqFrDWqFDK/Xy94dW5mTHDloqnCMGFMIOlkntTU1p5x++Xx+KisPY7P1EWIIITc396SlcXw+H057HxGGOJoaG1EgDkqWemtREqFaFcqUODbu2o5KF0J6eL/wyAWB4pRQIkPU1PfY2Xy4jbx4I/FRKqJikjlU387Ggw2UNNsIDwtjYuEsrpxZjMF48oXclpYW/A4rSZHDb4kXpESzYvdOrrzqaoYTE5lMTn7+BPLzJ5y0n7PFuBEfQRCUwDPAH0RRrB7uj01i/OFyu3n1P88yI0E7KKTAiJOWrj5W7TjIsvlTB+Vg9geCmG0uYmJi6OuzYdKPXJ4mVK2gz24fsR2grKyMj95bQaQGQjVybJ4AH70rcsU11w/JHHgMhVyO3eHgcGUl8qAHrUKOxeZDRCDSqEetUuDyBTGplURoveypKGdGnJqJ8UZC1HIEBDrtXg602LhmQjQKuQyfHMINOi6ZlE5WQhTrj3Tz8Le+d4qFXpHOzk7sdjudnZ0YtaphhRggRKvG5/Xg9/tRKEYW1vPFuBEf4IeAGvjNaE4WBOEh4CGAxLjR1VqSOP+U7t9PtDowJJbJFBYBAR/Ntl4aO3tIjTkuMGUNZuLTsogID8doMmHpcxI3wvau1eXDeJIRQ1VVFR+/+wZLJyUSYTi+wNtjd/Hxh8vRaLTk5+cNuW7fvn34vR56nXKywtVoVf3TOpc3SIfVTlCmwOUXMWq1eFxd+L0ebG454Ufz/QRFaLK6yIsNQaeS0+fxI8qOx0HFhhuI1Vk4cKCMadOmD2t7U1Mjqz9Yia2rnVCtis5eBx1dXRQmhhEbPnR3q8fuQqvTf6mUp+eCMXEyFAThMkEQxFH8fH70/EzgJ8A3RVEcVaYiURSfEUVxmiiK0yLDTOfuYSTOiKqKQ6RHDRMhbjKh1hkJV8vYW9WM3eXB3NPHhoMNVNsErr/pFgCKZs6lrLFr2IoPrRYbHpn6pBVNN6xfw5yMyEHCA/2evZdkRbNh/cdDak8EgkE+W/sR10zPprzDRVOvm0AgCAhoVXKCwSA/ef8gH5S20txpprq9h7QIHT0uP409LoJBERDpcfpIMKoJiCIOb2CIE198qIa25qZh7W5pbua1/zxNrt7DspmZXFWYwl2X5FKcauKdTfvo7HUMuWZ/vZmi4tmcaUbDYPCYrWe2WTVWErgNGPp1MhTn0X//BnwG7Di62wWgAoSjv3tEUXSdbSMlzj1BMTjiNCEuPp5Gq5u6Tj8fHjSj0ekonL6Qm6dNH8i7PG36NA7uL+Hzgw1My4zDoFUTCAapbbOws6GHG792L7IRFptttj662ltITh8+JCAxIpTNR6qxWCxEhB8feTU3N6MRvWTGJ2HQqlm7u5y9jVbijBrs3gBWtw8EAYNWTUu3HY0uhMQQD9F6JZtrLCSHaUkL1+LxBel2+BABhUzA7/PhdrvRHHWi9Pj8qNTDO1Su+3gVM5KMZMYfr00vl8mYMSEbt+cgq3eWc8fCqSgVcuwuD3vrzNjkodw895IT7iRisVjweDyEhYWdNIK9paWZzRs+pepwOYhBjGHhFM+dz/QZM0f8jE/GmIiPKIpO+rfLR0s+kAL0DNPWA/wV+M6ZWyZxvknPyqGxZMOI2f063QK33nEnEycMvwiqUiq55/4H2fDZp7y3awdKIYDHHyA+OY1l995KakrKsNcB+Pw+1ErFkJpexxAEAZVSjt/vH3Tc6/EMxEDFhBm4a/EMymvq6eiyEB+uJDlCjzsgIzstiTpNNDNzcujc9ylqhYyr8qNptLo42GbH4Q1Q0+0kPTICqyuAXq3E2tNDbFwcwaBIdZeLm6+ZOMSuPpuN9qY6Fs0euoiu0WiYM2UCT6/bx0tbjqBVKQgKCgqnz+T6BYsGiUtVVRWfrlmFrbsDrUpOnztI3uRCrrjyKnS6wQn3q6ureOfVFyhKNFA8OxOFXIa5x87uDatoqKvllmW3j1gFYyQujMnfqVkGnPgV8CRQBNwCNJ93iyTOClOmFrF1wye0dNtIiBi8NnOkpROnTEte3skHyWq1miVXLmXR4svp6+tDpVIRMopKmkajER9yeh3ugUjsL2J3eXD5RcLCBgtjVFQUnX0efIEgSnl/vuQJmWmkxUdj7emhrrsXn0zNxHlXkxtVhE0Zxisl20gO+nH5YWKskYL4/oXz9w92sKfRRkZUCKEhWvo8LvyBIFsrmwlPSicpKWmIXQ6nA71aOaL7gF6rJSE2hjse/CZ6Xb+39IlR7RXl5Xz49svMyYgiJbs/XYjb62dvbQUvPNPIA498E/XRUVcwGGDlW69zWW7MoBxEseEGlpr0fFByiPLyciZMGCqUJ2NcBJaKorhDFMXPv/gDtNM/3fpcFMWznx1b4rwQotdz2933saGqmw2HGqlrt1Dd2sX6sgb2mr3cee8Do/bRUSoUhIeFjUp4jp1fVDyHXTVtQ9aMRFFkd007k6cVD9lyN5lMJGVkU1bfPui4TqcnNi4es1/DLV/7OsXFM1EqlSQnJ5OQmU+9Q44vAC29Ljr7PLTZPDh9QXY09LKqopN1h9rZU2/hje1VyCLTWHbHnQy3PhMaGord48fj8w9pA3B6fLj9IuHh4RiMxiHCEwwG+Oi9d1iUFzfgiAigUSmYnZuEMWBj586dA+cfOXIEg9w/SHiOIZfJKEgMY/f2LSN/0CMwLsRH4uImLTWVx7//JGnTF9EohtEqj2bi/Kt5/In/Ijoq6pz2PX/+QghL5KO9tdSbe7A53TR2WFm9rxaPPprLLh8+A+B1N9xMTZ+MtftqKKtro8HcQ73Zwgd7anCqwnh3/SYe+P5/09LaAgjcfufdqONzWF1pocku8lFFN2/tb8eglnN5XjQzU8Lp7HPTHtByzyPfZtmd9wyMPE5Eq9WRlT+Jg40dw7bvr20jJTMHn883bHtdXR06wUts2PDxXpOSoti7Y+vA7z2WHiJ0I0+SokL1WLpOP0f0eJl2DUEUxa+PtQ0SZw+9TsfcuXOZO/f0itqdKUqlgru+fj8HDhygZMdWelu6MRhNTFt8AwWTJw9Jk3oMQSYQGR1DRWkJTW1mXF4/Nk8QpVrLhBQFh0oraeruQ6Z5D3lUOhkZmdz/jUf5j1zBzi0b0MiC3FmUQKRBjc8vYnV5iciOpBM9WzZ+zi3L7jip3ZdfeRXP/+sf+CqbmZgSTYhGhcVmZ2tZNWUtPaQne/jr735FamYOi6+8iqio6IFr+/r6MGpG9vMxhWjo62sZ+F2r0+L0jryz1ef0oBvlaPOLjFvxkZA4WyjkcqYUFjKlsHBU57vcbl549l8kqtw8tHgKSoUcW5+Nqpp6ytr6SArV8OT1xTi9PpqFGJa/8h/ufOAREhOTiI2NxahRMj1BT4RehSiKyGQQYdBg94pEGrSsKy+jt3fpsEGgxwgNNfHgo99i4+efsaJkF36fB7O5g/SYML55dTGhei0+f4DDzW288O9/8PVvPEZ0dMzRa0OxOocfFQF09zkxmY6vc+Xm5rJ65XLsLs9A6tYvcqi1hylzrxzVZ/dFpGmXhMRpUrJnN6agnRlZiQOpI7o7OsiIDmHphFh2Ha4jOdLApOQo4iNNFCUZ+PzT9fh8XspKdqFVKcmOj0ChVqNQqtFotWi1OsL0Guw2K/Ghaurr609ph8Fo5Oprr+fJn/2SzLwClhTlsGx+IaH6/h0tpULOpNRYJsdoWP/x6oHrUlNT8Sm1NHf1DrmnKIqUNXZRVDxn4JhGo2Xe4iV8XNZId59z4LjPH2BHZTNOhZHCKcN7gp8MaeQjIXGa7Nu9g9mJxz2yPR4PwYAftVKHWglReiX15h6yEvp9cLLio9ixtRKz2YxWAR65DJlMGOJprFDIEUSRYGDkig/D4fV6qa86zLKZwztT5iRFU7K9Eru9j5AQA4Ig49qbbmP5K/9hhttLZnwEcpmsP8l9bRs+fTTTZ8wYdI+5cy9BrVSx7pO1aIR21Ao5XXYvGbkTuPf6G04RAjI8kvhIXNQERZHq6mpqqo4AkJqeQU529pdyijuG024nRHvcLSAYDCL/gq9QiFrOrpo2jph7CU2OJExrQHG03eMPkBhpor7bTl6cafCNxf7aXS29Hm5ISR29PU4nGqUMUYQD9e3YnG50aiVZ8VGEaFUo5TL0agUOh2MgqVhGRiZ33PcwG9avYce2KlQKOX5RzpTpxcxfdBlKpeqEXgSmzyymaPp0mpub8fl8REdHD5ueY7RI4iNx0WK1WnnlxefB0UNquBYE+Gz/dtapjNx53wODvJZHi6WnB7lSSbO5m6zkGGSCDJVKhS8QJBgEmQy6HT52N1potzq58/p8gmoDSo2W+PgEdMYw4rReSirriTfpCNUef8mdHi+HzA5yCuZiOkXlhy+i1+sxW3p5ed1OkkwawnQKunsDlByuZ2JGEkWZ8Tg8fkL0g9PBJicnc8/9D+F0OvB6vYSEhJwy4FQmkw9Kl3EmSOIjcVESCAZ5+YXnyNT7KMg/nmJiSjqUN3Xw0nNP8/gTPxhxN+tEbLY+3n93Oc21VRDwsqW1B5m7l4ioaMLDwwkxGLE5HTT0uKjvsiMP+gnXyKgzW7G0+5g270pkMhnzFy9l7YpXmZSexKoDTaRHaok1qOlz+9jTaCUqLY9rb7jptJ61sbEJWcDLgswwEiOOb58Xef2sLW+lo9dOauYE9CHD56LW6fRDPJrPB5L4SFyUVB6uROnppWBC+pC2/KRoGrvrKS8vZ3JBwSnv5XK7eeGZf5Gq87FsViYyAT7eVUFZSy9pLg+iGCQsMorlG+pp7bZzSVY0OXGheP0B1lvklDZamH90lDVx4kRczhv45OMPiImNwdzXx+FOG86AwOKrbmHpVVdxssBPj8dDael+ykp24XY5iYqNp7WlmcsmpyHz9NLrcGPUqhFkAmqlnCmJRt490MFTD/3gS3+W5wpJfCQuSiorDpEeMfK3eVqknsqDZaMSn5I9ewgTHBRlpg4cu3JGHvtrWymtaWHN4VLCIqJwuwVunTsBwe+hx+lDqVKSmRxHSqaBj959m6ysLHQ6PdNnzKRg8mQqKiro6+sjNDSUvLy8YdZZBmO39/HCs/8ixG9nYoIJXbSeps5Gag+XM+/SCYTHptLV2UmL1YZMJhAMihiMRtKS5ASDp7eIfT6QxEfioiQoDl4EPhG5TCAwyl2l0pKdzEgYvD4kl8koykxkakYCa0vrEU2JhHvNpCX3pxTv7nPh8wdQKzSEGvQkm1SU7i9l1uzZAKjVGgoLT1WCWKS1tZW2tjbkcjn7du8kWe1l2oTUgTO0KgW7y9U4bT3o9Xri4xMIBOMI+P3I5XLkcjna9rpRP+v5RBIfiYuS1LQMyjaUkzs0LhOARouDzOLRVdd0OZ3oNcOHeQiCgFEtp6G7g/zE4yOtP6/aQ3V7D7dfG0a63kiMQU1He+uo7e/psfDOm69h62glPlRDr9NDRW0jd16ShyiKA/FYWpUSjVqNLwg93d0YDAbkMhlyVf8oyuH2YnUHiDrHYSpfBsnJUOKiZFJBAZ0egQbz0CwszV29tDpEpkwpHNW9oqJj6bD2jdje6fRjCA3F6e33Gvb5/agFP3p5EJulm5qqSlrNnQRHmXvL5XLx4rP/JlFu57biTOblJ5EbZyI/zojf0YvZfDygVRAEJmUkcKDFhsPZn0DsWHE/URTZWdXK5GkzR4wTG0ukkY/ERYlKqeSOex7gtReeobbLRnqkEUEQqO+y0WgLcNvd9w0kJDsV02fNZf27r5ISE47yhMqezV1W3IKGJfMX8fl7r5MRY6KpsYEHixMwaNMpF2PwI+OTykbUnr3MX7iQsLCTb/Hv37eXSLmHgtTjW9oymYxAUCTSqKOlx0pERCTKo9H2BalxtHZa+fhQGy02DwaVDJcvSGMfhKfmsGzJ6Yc+nA+kkY/ERUtiYgKPf/9J0mdcRpVXT6VbS+LUBXzziR8OKYVzMvLy80jMmczqvXU0dVkRRRG318/+2jY2VHVz47KvkZubizIsjrW7K9DIgoTqNchkAv6AyJbqLpJiIpkcq2X9mtWn7O/Avj3knOCAmBhppMPhw+Xzo1PKsH8hKb6IiCLoQaZU0xdQ0GAL0uNTYDJo6e2x0Nc38qhtLJFGPhIXNTqtljlz5jJnzpePlheAG26+hX17M9ixZSNrDlSgUCjJLyjkvhvnExPdHzF+y+1f42dPfp/2HjuJJg0icCgYgcpg4tJJGQRFkdd3HGDfvr34fP2J7bOysobk2/F63GjUg3fq1EoFk9IS2FDZzozkUIJfyD90uLaRg61W7rpsBlGhg68rq29n1fsrufveB770858rJPGRkBgFMkGgqKiIoqIigkcXfE/cS/P7/aQmxHJZfhy/XrGNdquTxQunMHViJnKZjIqmDtpbW9m+ejkRIRp6XAE+8Mu45sZbyftCVdXYhCTauuuHJLWfkZOMLxDgrX31TEoXCO900On0U1rVwPXF+UOEB/p9mt7YXo3Vaj0tr+nzgSQ+EhKniWyEhPc6nQ5zj42VWzro6Omjx+Ghqa2Tg227yYyPorq5nctzIpg8KQ3F0dGO2Wrnw7dfQXPPg6Sl9TtEzpg1h7deOEBGfARa1fFwB5lMIDkqjHqHnPz5V+Dz+UgJCaGj7y3yk4cvD6WQywgLUdHTY5HER0LiYqW1pQXR62J2ppHFOZH4AkHM2iR6PEHe2FnN5fnRxESEDQgP9FdVLU4L5/P1a0h76FEAkpNTmDZ3Ee9v+ZTCxFASo0z4/AGOtFqo6vFy570PkZjY70MQCPhZvXI5bq9/IKn9FxFFEYfHj06nG9I21kjiIyFxFhCBT9es5sqpGQhOK1qFQFSIFktQjtovw6iRo5ZBVHT0kGtTY8LYsrUKh8OO/mjw54JFl5GcmsqOLZspKa1DoVSSXzCFh2bNHrRbJpcryC+YQnlTFVMz4ofcu7m7F1WIiZiYC69wpiQ+EhJnAavVirWrnczsLLxeE50dZqw9DqxKLy02DyFqJSqlCrlcPuRauUyGSiEfknM5IyOTjIzMU/Z96cJFPP/PQ+hUnWQl9OfmEUWRlm4bGys7ufHOeznTQoHnAkl8JCTOAv21vOQIgoBareZQdwBzr5+4dC15Oansb9mLXxQHHAC/iNXhIiBTYDAMn9D9VERERHLPQ4+yauUKSnbWEKFTYXP7kWuN3PC1r5OVNTpP7vONJD4SEmcBk8mEwyvicHvRa1Rsrmimur2HO5OnEaLVkBITwcFWCzm5g185URTZU2OmqHjOkC330yEmJpb7H36Mrs5OeqxWdDotCQkJXIgjnmNI4iMhcRZQq9UUTJ1BSe1+LslL4orCVGY44gg1hPTHYimVWBVhrC+rZ1JiBKEhWnrsTsoaLSgjEvtL+JwFIqOiiLwA47iGQxIfCYmzxOIlS3jx2fp+gUmKICxEx3arwNrSOoIhMfzXI/dyqPwQe3dsxVbbhiksnKLF1zN58uRTZhA8RkeHmfJDB/G43ETFxjJx4kRUqtPPn3whIJxYqfFiZMqEXPGzt54dazMkvgJ4fT727N7D3p1bsdmsuCOmkzJtIUVF01AoRycwwxEI+HlvxTvUHNpPRqQOrVKO2e7F7BS56fY7L9h1HYB5k8JLRFGcduJxaeQjIXEWUSmVzJ49i+j4RJwuFz3yZCKyh7x3p83Hq1ZhrT/IrcWZg4Jb2619rHj9Re59+FvExMSecT/nEymwVELiHPDnZ1/hOz//A22nkcNnJBwOO2UlO1kwIXlIVH2sycCkGD3btmwa4WoRs7mdqqqqo6k4LpyZjjTykZA4B2SkJiFXyNGoR5e242TU1dURb1SjVg7/umbGRbCy9CDcdOug442Njax+fwUOSycmvQqrw4s+PIql19141ipQnAmS+EhInAOeePAuAHY2nvlWdzAYRC4f+T4KuWxIjuaWlmZef+FpZqeaSM/OQBAERFGktr2H1//zNHc9+CgJCYlnbNuZIE27JCQucBITE2npcREYxkERoL6jh+TUwdVKP1mzmumJBjLiIgZSrgqCQEZcONOTjHyy5qNzbvepkMRHQuICJzw8gsSMbPZUt3Hi7rTD7WV/k5VZl8wfOGa399HSUENmfOSw98uMj6S1oQ67fWyTjEniIyFxDvj+r/7ENfd+i4bGurNyvxtuvhVzUM/H++qoaeum1WJjb00r7+1tYMaCJYNiwNwuN1qlEoV8+NdbIZehVSlwu9xnxbYvi7TmIyFxDnC53DicrmFjub4Men0IDz76OOXl5ZTt3Y2nx0V0/ATuvqGY2Ni4QecajAZc/uCIaTbcXj9ObxCDcWgsWTAYoLKykq6uLlQqFfl5+RiMX74e+8mQxEdC4hzwh58+QSAY5EDH2asaoVAoKSiYTEHB5JOep1ZryCsoZH/dYYpzhi4ql9a1kze5cEhFi/r6Ola88Sp6wUuMXoHTL/LZR+8zpXg2V1x5FYJwdidKkvhISJwDdNr+F/tku1TnksVXXMnz/65lc3kjk1OjMeo02JweyhrMtPvU3H/F4IoWXV2dvPnS8yzIiiAxMnTguMfnZ13pDj5VKLns8iVn1cZxteYjCEKCIAj/EQShXRAEjyAIdYIg/Gas7ZKQuNAICTHw4KOPE5Yzg/fL2vjPxgreL2vFlD2DBx55nJCQwVOubZs3MSFaM0h4oD9x/aKJSezaugm323VWbRw3Ix9BEFKBrUAd8C3ADKQCp862JCFxnnnx7fdpMXcyefa16JMnjIkNOp2eK65cyhVXXonP5zta52v4kVj5gVJumDw0EyKATq0iJkRBXW3doET3Z8q4ER/g30ALsEAUxWMp3zaOoT0SEiOyu/QQFdV1ZEw+O6kyzgwBpVJ10jP8fh8q5cgTIZVCjs/vP6tWjQvxEQQhA7gCuPsLwiMhccFy9y3XYLPZESKG5my+EElITKaps5eMuIghbYFgkLZeF1fGxQ1z5ZdnvKz5zDn6r0sQhPVH13t6BEF4WRCEoZ+WhMQYM7NwEovnzfrSqVHPN8VzL2VvYw8e39DRTWm9mZik9LOepGxc5PMRhP/f3r2FWFXFcRz//h4isqIyhULKEku6IBURkpHiBaKHUuilIqMIEdLyISzBSi2bXrIiyhLpShChRhoIWWI95EB2bzIv1BiCQl4wTbOL/x7WGT2dZmqsfc46e/v7wHBm9p4D/83Ab/ZaZ+3/0gNAB7APeA1YRprr6SDNAV0VEYcb3jMVmFr7cQSwsWUFF28QsDN3ERn5+st9/UMj4m/JlWXYJWkCsLofv/pBRIzl6B3a2oi4u/b9Gkl7gTdIQ7JV9W+MiMXA4mIqzkvS+t6aMR0vfP3VvP5ccz4fARf14/cO1F531V4bA+vd2uvlNISPmbW3LOETEQeAb4/hLV3/cr6YNexm1jJlmXDuBHaQhlf1epZcftzaclquEsPH/8HXX0GlmHAGkHQ78DLwArCcNOG8APgcGBdluRAzA0oUPgCSbgPuBy4AdgNLgdkRsT9rYWZ2zEoVPmZWHWWZ8zFA0oWSnpb0paT9krZLWiHpn3sslJCkcyQtlbRX0k+Slks6N3ddrSDpJknLJG2VdFDSRkkdksqxYrGffOdTIpKmkxZOvgJ8CpwOzAIuA66JiE+yFVcgSQOAL4BDwBzSfi+PAgOAkRHxc8bymk5SJ/AD8DawjbSUZC7pE+KrGxfUlpXDp0QkDQJ21U+uSzoN6AZWRsSUXLUVSdK9wEJgRERsqR07H9gMzIqIhTnrazZJgyPix4ZjU0j/dMZHxJo8lRXLw64SiYidjZ/qRcReYBMwJE9VTXED0NkTPAAR8T2ppcqN2apqkcbgqelZTlKZv7PDp+QkDQQuBTbkrqVAlwBf93K8CyiuoUy5jKm9Vubv7PApv2dIHaKeylxHkQYCe3o5vhs4o8W1ZCdpCDAfeC8i1ueupygOn4wkTZAU/fha28f7ZwO3ANPrhyhWHZJOIU08/w7ckbmcQpWimViFHesDtkdImgY8BsyJiBeLLiyzPfR+h9PXHVElSToJWAkMA8ZExLbMJRXK4ZPRf3jAFjiy0vs54ImIWFB4Yfl1keZ9Gl0MfNPiWrKQdAJpBf+VwMSI+CpzSYXzsKtkJE0GXgKWRMR9uetpkhXAKEnDeg7UNhAYXTtXaUobZL0OjAMmRURn5pKawut8SkTStaQeRl3ADP7aSuRQRHyWpbCCSTqZtMjwIEcXGT4CnEpaZFjpZ/kkLQKmkR6cfqfh9LaqDL8cPiUiaS7wcB+nt0bEea2rprlqj1I8CUwkfZr3PjAzIrpz1tUKkrqBoX2cnhcRc1tXTfM4fMwsC8/5mFkWDh8zy8LhY2ZZOHzMLAuHj5ll4fAxsywcPmaWhcPH2oKk4ZJ+kzS/4fgiSfskVW674OOdw8faQq0lyBJgpqQzASQ9BNwJTK5SHxtLvMLZ2oaks4EtpCf2N5I2iLw5It7MWpg1he98rG1ExHZSR8YZwPPAPfXBI+lBSZskHZY0KU+VVhSHj7WbzcCJwLqIeLbh3GrgOuDDlldlhXP4WNuQNJ401FoHjJY0sv58RHRGxHdZirPCOXysLUi6AniLNOk8lrRpXkfOmqy5HD6WnaThwCpSo7QZEfErMA+4vtZAzSrI4WNZSTqLFDobgFvrtgJ+ldTf+vFctVlzuYG8ZRURO0i7MzQe/4P+7exhJeV1PlYatTaydwGDgX3AL8CoqvQ0Pt44fMwsC8/5mFkWDh8zy8LhY2ZZOHzMLAuHj5ll4fAxsywcPmaWhcPHzLJw+JhZFn8CXkJ6QCmDsrcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean test accuracy 66.67%\n",
      "Is drift? Yes!\n"
     ]
    }
   ],
   "source": [
    "shift_norm_0 = multivariate_normal([2, -4], np.eye(2)*sigma**2)\n",
    "X_0 = shift_norm_0.rvs(size=int(N_test*phi1),random_state=2)\n",
    "X_1 = ref_norm_1.rvs(size=int(N_test*phi2),random_state=2)\n",
    "X_test = np.vstack([X_0, X_1])\n",
    "\n",
    "# Plot\n",
    "y_test = true_model(X_test,true_slope)\n",
    "plot(X_test,y_test,true_slope,clf=clf)\n",
    "\n",
    "# Classifier accuracy\n",
    "print('Mean test accuracy %.2f%%' %(100*clf.score(X_test,y_test)))\n",
    "\n",
    "# Check for drift in covariates\n",
    "pred = detector.predict(X_test)\n",
    "print('Is drift? %s!' %labels[pred['data']['is_drift']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test data has drifted into a previously unseen region of feature space, and the model is now misclassifying a number of test observations. If true test labels are available, this is easily detectable by monitoring the test accuracy. However, labels aren't always avaiable at test time, in which case a drift detector monitoring the covariates comes in handy. In this case, the MMD detector successfully detects the covariate drift.\n",
    "\n",
    "In a similar manner, a proxy for prior drift can be monitored by initialising a detector on labels from the reference set, and then feeding it a model's predicted labels:\n",
    "\n",
    "```python\n",
    "label_detector = MMDDrift(y_ref.reshape(-1,1), backend='tensorflow', p_val=.05)\n",
    "y_pred = clf.predict(X_test)\n",
    "label_detector.predict(y_pred.reshape(-1,1))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concept drift \n",
    "\n",
    "Concept drift detectors are coming soon!"
   ]
  }
 ],
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